A Unified Model of Localized Corrosion of Passive Oxide Layers
The passivation of metals and alloys against further corrosion and oxidation is usually achieved by the formation of compact oxide layers on their surfaces. However, the localized corrosion (also called pitting corrosion) can occur under certain aggressive environment to break the passive oxide layers and result in further material performance degradation. There are still debates on the dominant factors (pitting initiation vs. pitting stabilization) that control the localized corrosions on various types of metals/alloys due to the complex electrochemical reaction mechanisms at multiple time and length scales. Thus, it is critical from both scientific and engineering aspects to construct a unified model of localized corrosion that can consider multiple factors (pitting initiation and stabilization) to improve the corrosion resistance of structural and functional alloys.
The short-term target in this Mcubed project is to construct the multiscale framework with experimental verifications that have the capability to simulate localized corrosions for several model metal/alloy systems (pure Al and Al binary alloys) and identify the dominant factors that result in the localization and acceleration of the corrosion reactions under different electrochemical conditions. In this framework, thermodynamic and kinetic parameters of the passive oxide layers and interfacial reactions under electrochemical conditions will then be estimated by a combination of first-principles calculations, stochastic simulations, and CALPHAD (Calculation of Phase Diagrams) methods. Based on these parameters, a 3D phase-field corrosion model will be applied to consider the diffusion kinetics in passive oxide layers, interfacial (electro)chemical reactions, and the potential distributions through the electric double layers, affording predictions for corrosion pitting at the mesoscale under various conditions. In-situ electrochemical data from ensemble and local potential measurements will be collected and used to inform the models and test these predictions. The long-term target is to extend this framework, with the help of machine learning and advanced characterization methods, to multiple commercial alloy systems to design passive oxide layers with enhanced protectiveness.
Presented at the Materials Research Society, Boston, MA